Poultry Science · 2006 · 97 citations · 27 references
AgricultureGeneticsPoultry SciencePoultry BreedingPoultry GeneticsQuantitative GeneticsBroiler BreedsBroiler GeneticsBroiler Growth PerformanceGenetic ParametersQuantitative Trait LociAbdominal Visceral FatGenome-wide Linkage AnalysisMicrosatellite MappingMicrosatellite MarkersMulti-trait Qtl MappingPhenotypic Data AnalysisPhenotypic Trait VariationQtl Detection
Two informative chicken F(2) populations based on crosses between a broiler breeder male line and dams from genetically distinct, highly inbred (>99%) chicken lines, the Leghorn G-B2 and Fayoumi M15.2, have been used for genome-wide linkage and QTL analysis. Phenotypic data on 12 body composition traits (breast muscle weight, breast muscle weight percentage, abdominal fat weight, abdominal fat weight percentage, heart weight, heart weight percentage, liver weight, liver weight percentage, spleen weight, spleen weight percentage, and drumstick weight, and drumstick weight percentage) were collected. Birds were genotyped for 269 microsatellite markers across the genome. The QTL Express program was used to detect QTL for body composition traits. Significant levels were obtained using the permutation test. For the twelve traits, a total of 61 (Gga 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 18, 24, and Z) and 45 (Gga 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 15, 17, and E46) significant QTL were detected at the 5% chromosome-wise significance level, of which 19 and 11 were significant at the 5% genome-wise level for the broiler-Leghorn cross and broiler-Fayoumi cross, respectively. Phenotypic variation for each trait explained by all QTL across the genome ranged from 3.22 to 33.31% in the broiler-Leghorn cross and 4.83 to 47.12% in broiler-Fayoumi cross. Distinct QTL profiles between the 2 crosses were observed for most traits. Cryptic alleles were detected for each trait. Potential candidate genes within the QTL region for body composition traits at the 1% chromosome-wise significance level were identified from databases for future association study. The results of the current study will increase the knowledge of genetic markers associated with body composition traits and aid the process of identifying causative genes. Knowledge of beneficial genetic variation can be incorporated in breeding programs to enhance genetic improvement through marker-assisted selection in chickens.
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Makoto Miyazaki, Mark J. Jacobson, Weng Chi Man et al. · Journal of Biological Chemistry · 2003 · 205 citations · Full text
Quantitative trait loci affecting fatness in the chicken
C. O. N. Ikeobi, John Woolliams, David Morrice et al. · Animal Genetics · 2002 · 172 citations
Breast meat quality and composition in unique chicken populations
N. Deeb, CA Fedler, S.J. Lamont · Poultry Science · 2003 · 158 citations · Full text